# Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation Source: [Adversarially Domain-adaptive Latent Diffusion for Unsupervised Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:28:27.917Z Authors: Jongmin Yu, Zhongtian Sun, Chen Bene Chi, Jinhong Yang, Shan Luo Published: 2024-12-22T04:55:41.000Z ## Abstract Semantic segmentation requires extensive pixel-level annotation, motivating unsupervised domain adaptation (UDA) to transfer knowledge from labelled source domains to unlabelled or weakly labelled target domains. One of the most efficient strategies involves using synthetic datasets generated within controlled virtual environments, such as video games or traffic simulators, which can automatically generate pixel-level annotations. However, even when such datasets are available, learning a well-generalised representation that captures both domains remains challenging, owing to probabilistic and geometric discrepancies between the virtual world and real-world imagery. This work introduces a semantic segmentation method based on latent diffusion models, termed Inter-Coder Connected Latent Diffusion (ICCLD), alongside an unsupervised domain adaptation approach. The model employs an inter-coder connection to enhance contextual understanding and preserve fine details, while adversarial learning aligns latent feature distributions across domains during the latent diffusion process. Experiments on GTA5, Synthia, and Cityscapes demonstrate that ICCLD outperforms state-of-the-art UDA methods, achieving mIoU scores of 74.4 (GTA5$\\rightarrow$Cityscapes) and 67.2 (Synthia$\\rightarrow$Cityscapes). ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()